A hybrid anomaly detection model using G-LDA

Anomaly detection is one of the important challenges of network security associated today. We present a novel hybrid technique called G-LDA to identify the anomalies in network traffic. We propose a hybrid technique integrating Latent Dirichlet Allocation and genetic algorithm namely the G-LDA process. Furthermore, feature selection plays an important role in identifying the subset of attributes for determining the anomaly packets. The proposed method is evaluated by carrying out experiments on KDDCUP'99 dataset. The experimental results reveal that the hybrid technique has a better accuracy for detecting known and unknown attacks and a low false positive rate.